Abstract 208P
Background
Prognostic markers in routine clinical practice of breast cancer are currently assessed using multi-gene panels. However, the fluctuating tumor purity can reduce the predictive value of such tests. Immunohistochemistry (IHC) holds the potential for a better risk assessment.
Methods
To enable automated prognosis marker detection (i.e. HER2, GATA3, PR, ER, and AR, TOP2A, Ki-67, TROP2), we have developed and validated a framework for automated breast cancer identification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of 11+1 marker BLEACH&STAIN multiplex fluorescence immunohistochemistry (mfIHC) staining in 2′004 breast cancers.
Results
The optimal distance between Myosin+ basal cells and benign panCK+ cells was identified as 25 μm and used to exclude benign glands from the analysis combined with several deep learning-based algorithms. Our framework discriminated normal glands from malignant glands with an AUC of 0.96. The accuracy of the approach was also validated by well-characterized biological findings, such as the identification of 13% HER2+, 73% PR+/ER+, and 14 triple negative cases. Furthermore, the automated assessment of GATA3, PR, ER, TOP2A-LI, Ki-67-LI and TROP2 was significantly liked to the tumor grade (p<0.001 each). A high expression level of HER2, GATA3, PR, and ER was associated with a prolonged overall survival (p≥0.002 each).
Conclusions
A deep learning-based framework for automated breast cancer identification using BLEACH&STAIN mfIHC facilitates automated prognosis marker quantification in breast cancer.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
G. Sauter: Financial Interests, Personal, Other: MSVA Antibodies. All other authors have declared no conflicts of interest.